This research paper presents a robust facial emotion recognition system leveraging deep learning models to accurately classify human emotions from facial expressions. The proposed system utilizes convolutional neural networks (CNNs) trained on standard emotion datasets such as FER-2013 and CK+. Our approach demonstrates high accuracy in real-time emotion detection across diverse facial expressions including happiness, sadness, anger, surprise, fear, disgust, and neutrality. The study highlights the effectiveness of deep learning in achieving scalable, non-intrusive, and real-time emotional intelligence for applications in human-computer interaction, mental health assessment, and security systems.